CN102448368B - Method and system for providing behavioural therapy for insomnia - Google Patents

Method and system for providing behavioural therapy for insomnia Download PDF

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Publication number
CN102448368B
CN102448368B CN201080024189.8A CN201080024189A CN102448368B CN 102448368 B CN102448368 B CN 102448368B CN 201080024189 A CN201080024189 A CN 201080024189A CN 102448368 B CN102448368 B CN 102448368B
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patient
sleep
processing unit
data
sensor
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CN201080024189.8A
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Chinese (zh)
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CN102448368A (en
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E·瑙约卡特
S·德沃特
W·霍斯
H·张
A·J·J·拉德马克斯
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皇家飞利浦电子股份有限公司
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Priority to US61/184,165 priority
Application filed by 皇家飞利浦电子股份有限公司 filed Critical 皇家飞利浦电子股份有限公司
Priority to PCT/IB2010/052440 priority patent/WO2010140117A1/en
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118Determining activity level
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Measuring bioelectric signals of the body or parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/103Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A61B5/1135Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing by monitoring thoracic expansion
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4809Sleep detection, i.e. determining whether a subject is asleep or not
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4812Detecting sleep stages or cycles
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802Sensor mounted on worn items
    • A61B5/6804Garments; Clothes
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/68Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Social work
    • GPHYSICS
    • G09EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09BEDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00Teaching not covered by other main groups of this subclass
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4815Sleep quality
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • Y02A90/22
    • Y02A90/26

Abstract

The disclosed system and method provide for the automatic assessment of the presence/severity of the sleep problem and its exact nature. The assessment is based on qualitative information about sleep patterns, insomnia-related factors and daytime consequences, as well as quantitative information about sleep patterns measured by a sensor. By combining the different sources of information (subjective as well as objective data), the diagnosis gives more insight into the nature of the sleep problem and is therefore more accurate. Furthermore, the disclosed system may be used to select specific components of the system that are medically relevant to the individual and therefore create a personalized program. The system teaches a selection of self- management skills that could help the individual to better cope with sleep disturbances and target those factors that maintain the problem or make it worse by a particular individual.

Description

For providing the method and system of the behavior therapy of insomnia

The denomination of invention that the application requires on June 4th, 2009 to submit to according to 35 U.S.C. § 119 (e) is the priority of the U.S. Provisional Patent Application No.61/184165 of " System and Method for Managing Insomnia ".

Technical field

The present invention relates to system and the correlation technique of the cognitive behavioral therapy being constructed to the patient promoting to have insomnia.

Background technology

Sleep disorder is very general.The people of at least 10% suffers from clinical significant sleep disorder, and it exists material impact to public health.So far, insomnia is sleep disturbances form the most general.

Most of insomnia's definition comprises the description with relevant symptom uncomfortable in the daytime specifically for sleep.Sleep symptom generally includes difficulty falling asleep, maintains dyskoimesis, cannot sleep or expendable or that quality is totally very poor sleep after time much more Zao than expection wakes up again.When waking up, symptom is with uncomfortable relevant in the daytime, and it relates to fatigue, sleepy, emotion disturbance, cognitive difficulties and society or professional obstruction.

In population, the popularity degree of isolated insomnia's symptom is approximately 30% to 50%, and what about 9% to 15% report was caused by chronic insomnia problem hinders significantly in the daytime.Had a sleepless night by Drug therapy in the middle of the overwhelming majority of patient group.The market scale of sleeping pill is approximately 4,600,000,000 dollars.The other treatment form of limit treatment of such as sleeping is not too universal, although sign shows that they are more effective than independent pharmacotherapy on long terms.

Modal situation is that employing is a kind of for assessment of the character of sleeping problems and the standard diagnostic approach of the order of severity, that is, usually have so-called Sleep diaries or the sleep diary of the form of questionnaire on paper; Also activity change record substituting as Sleep diaries can be adopted.

The major defect of this Sleep diaries is that accuracy is subject to the impact of patient's subjective bias, such as, for being often difficult to remember exactly sleeping and time period of waking at night patient.

Vital sign is measured to the automatic testing requirement of sleep and awakening stage, but most of existing scheme depends on bonded-electrode regrettably, such as, they pasted or be glued on the skin of patient, thus electrode is worn on (such as) head, to obtain EEG.Owing to being connected to cable and the recording equipment of these sensors, these electrodes make patient uncomfortable so attract attention in sleep procedure.In addition, also have a problem: in the time of having a rest, cable or electrode may occur to connect and loosen, and it will reduce the quality of Received signal strength.

High by sleep mode automatically daily record diagnosis insomnia cost in the sleep experiments indoor with a complete set of equipment, can not in long-time section engaged position, thus for insomniac carry out diagnosing limited in one's ability.

In addition, extra sensor and the sleep of patient may be disturbed along the line that patient arranges, therefore affect night to sleeping and the assessment of awakening stage, it will cause impacting hypnograph, thus the diagnosis that may lead to errors.

U.S. Patent Application Publication text No.2004/0225179 discloses the automatism method and system being used for the treatment of insomnia, which employs passive means to determine awakening/sleep state, is incorporated to by reference herein at this.

Insomnia is ubiquitous sleep disorder in population.But in the middle of practice, patient tends to not seek treatment, and is often diagnosed by non-sleep expert (such as, GP), instead of employing standard, correct method assess character and the order of severity of sleeping problems.This result in underestimating of the fact to sleeping problems and/or opening of sleeping pill prescription usually, although the verified treatment without the need to medicine, that is, the cognitive behavioral therapy (CBT-I) for insomnia is effective equally, and more lasting.In addition, the treatment assessed more accurately and customize can obtain higher compliance, thus obtains better medical outcome.

Summary of the invention

The object of disclosed theory is to provide a kind of system being constructed to the cognitive behavioral therapy of the patient promoting to suffer from insomnia, and described system comprises: communications component, and it is constructed to provide electronic communication; Sensing system, it has at least one sensor, described sensing system is configured to detect sleep activity data provide the sensor system signals combining described sleep activity data, and described sensing system is coupled to described communications component and electronic communication with it; First processing unit, it is coupled to described communications component and electronic communication with it, and described first processing unit is configured to receiving sensor system signal and converts sleep activity data to sleep pattern data; Second processing unit, it has input module, and described second processing unit is configured to collect patient and inputs data, and described second processing unit is coupled to described communications component and electronic communication with it; 3rd processing unit, it is coupled to described communications component and electronic communication with it, and described 3rd processing unit is configured to receive sleep pattern data and patient inputs data, and to its execution analysis, thus create patient sleeps's overview; Fourth processing unit, it is coupled to described communications component and electronic communication with it, and described fourth processing unit is configured to analyze described patient sleeps's overview and provides the treatment course for the treatment of relevant to described patient sleeps's overview; And display, it is coupled to described communications component and electronic communication with it, and described display is configured to present user interface.

Another object of disclosed theory is to provide a kind of and provides the method for cognitive behavioral therapy for insomnia, the step that described method connects has: the sleep utilizing the sensing system monitoring patient with at least one inconspicuous sensor, and described sensing system is configured to detect sleep activity data; Collect patient and input data; Merge patient and input data and sleep activity data to create patient sleeps's overview; Analyze the sleep overview of patient to determine to treat the course for the treatment of; And the course for the treatment of is presented to patient in treatment over the display.

As used herein, term " patient " is not only applicable to the mankind, is also applicable to animal.In addition, term " patient " does not represent that corresponding people/animal is just sick, and therefore, Healthy People also will be called as " patient ".

Term " sleep/awakening classification " refers to being " waking " or " sleeping " classification studied epoch, described classification is obtained by the probability belonging to corresponding class, or as the "true" or "false" classification that the output of grader provides, also refer to the classifier result shown in user interface or any device.

In addition, term " pNN50 " refers to that the interval difference at adjacent NN interval is greater than the percentage ratio of the quantity of 50ms, term " SDNN " refers to the standard deviation at all NN intervals, term " SDSD " refers to the standard deviation between the distance between adjacent spaces, term " RR_mean " refers to the average duration at RR interval, term " HR_mean " refers to average heart rate, term " LF " refers to the low-frequency range that heart rate variability standard defines, term " HF " refers to the high-frequency range that heart rate variability standard defines, term " RMSSD " refers to root-mean-square successive difference, term " HRV " refers to heart rate variability.

Accompanying drawing explanation

With reference to the embodiments described below, these and other aspects of the present invention are by apparent and be elaborated.

In the accompanying drawings:

Fig. 1 schematically shows General Principle of the present invention;

Fig. 2 shows the indicative flowchart of the feature extraction of the first preferred embodiment of the present invention;

Fig. 3 shows the indicative flowchart of the sleep/awakening sorting technique of the first preferred embodiment of the present invention;

Fig. 4 shows the sleep limit algorithm based on method of the present invention;

Fig. 5 schematically shows key element of the present invention;

Fig. 6 shows the indicative flowchart of the method providing cognitive behavioral therapy for insomnia;

Fig. 7 shows the indicative flowchart arranging module relevant to the length of one's sleep;

Fig. 8 shows the indicative flowchart relevant to sleeping modules in associated bed;

Fig. 9 shows the indicative flowchart relevant to Cognitive reconstruction module;

Figure 10 shows the indicative flowchart relevant to coping strategy module;

Figure 11 shows the indicative flowchart relevant to loosening module; And

Figure 12 shows the indicative flowchart relevant to life style module.

Detailed description of the invention

As can be seen from Figure 1, the system 100 for classification of sleeping/awaken according to a preferred embodiment of the invention comprises the inconspicuous bed inner sensor 101 for vital sign monitoring, it monitors cardiomotility by ECG, and/or moved by foil in bed (foil) Sensor monitoring health, etc., hereinafter will be explained this.Another step between the stage of preparation of signal, carries out filtering and artefact removal by pretreatment unit 102, independent from ECG and/or health movable signal or extract feature from the combination of the two especially by feature extraction unit 103, afterwards, sleep/wakefulness is classified according to all input feature vectors by sleep/awakening grader unit 104, comparing of sleeping time and time is in bed calculated by Sleep efficiency computing unit 105, it is used as the input of patient sleeps's limit algorithm, device 106 provides (such as) to obtain the rule of more healthy sleep to patient.

Sleep/awakening classification 104 can be adopted, Sleep efficiency calculate 105 and the output of sleep limit algorithm device 106 provide feedback for patient 107 or medical professional, additional source from such as Sleep diaries problem is also obtained information by patient 107 or medical professional, to obtain subjective parameters 109.

With regard to the Sensor section of proposed system, following embodiment is possible: sensor is the ferrum electret foil be placed on below patient chest, moves for measuring heart rate, breathing and health.

Or, in another preferred embodiment, the piezoresistance deformeter on the lath below the mattress being glued to patient chest region can be adopted to move to measure heart rate, breathing and health.

In another preferred embodiment, only use ECG, the fabric ECG preferably combined with foot mat (footmat) electrode in medicated pillow and bed.

Substituting as ferrum electret foil or deformeter, can be with the breath signal measured to combine around the standard (inductance or piezoresistance) of chest and/or abdominal part with adopting ECG sensor.Such sensor can also be merged in fabric (such as, T-shirt), thus make it more unobtrusively.

Or, in another embodiment of alternative ferrum electret foil or deformeter, ECG sensor and accelerometer signal can be made to combine.Described device can be weared on wrist device, but is preferably positioned at 2D or the 3D accelerometer that being used on patient's trunk measures body kinematics.Also such sensor can be merged in fabric (such as, T-shirt), thus make it more unobtrusively.

Several step can be comprised by processing unit as seen from Figure 1.These are the pretreatment of classifying to initial data, feature extraction and sleep/awakening.In ensuing paragraph, by the different alternatives of each one described in more detail in these steps.

The signal conditioner 102 of Fig. 1 comprises one or more following step, can in turn, parallel or repeat described step:

To the suitable filtering of (one or more) signal; And

Artefact is removed.

With regard to ECG signal, the removal of systolia ectopica may be necessary.

With regard to the gap in signal, interpolation may be necessary.

Feature deriving means 103 comprises from ECG and breath signal extraction feature, comprising:

Following characteristic is derived from ECG:

The statistically heart rate variability parameter (such as, average heart rate, SDNN, RMSSD etc.) of time domain;

From the parameter of heart rate variability (HRV) spectrum (such as, LF, HF);

Multiple dimensioned Sample Entropy; And

Progressive trend eliminates fluction analysis.

According to the breath signal recorded by ferrum electret foil, slat sensor or inductance/piezoelectric electro stopband, calculate spectrum, and extract LF and HF power as feature.In addition, average breath rate is determined.

In addition, feature deriving means 103 allows the Coherent Power calculating two spectrums when both ECG signal and breath signal are all available as extra feature.In addition, the ratio of heart rate and breathing rate can also be derived as additional features.

In addition, according to ferrum electret foil or slat sensor signal, index of activity is derived by the basis of large health movement.

The preferred embodiments of the present invention also propose, and for next step, classification 104 of namely sleeping/awaken, index of activity and at least one additional features preferred compositions relevant to heart and/or breathing state work the input being used as categorizing process.

For studied each epoch (epoch), such as, each section of 1 minute of data generates vector, and this vector comprises at least one feature, the subset of preferred all above-mentioned features or at least above-mentioned feature.

In addition, this vector is flowed to sleep/awakening grader 104, this grader is based on the mode standard identifying schemes with supervised learning ability, will be explained this in figure 3.For grader, preferably adopt following proposal:

Linear or the quadratic discriminant grader of Bayes;

Support vector machine;

K arest neighbors (kNN) method;

Neutral net; And

Hidden Markov model.

The parameter of training classifier on the data base of large-scale representative data.

In order to receive patient to Sleep diaries, insomnia's severity index, insomnia's frequency questionnaire or other assess in the relevant input of subjective questions and provide feedback to it, preferred in a preferred embodiment of the invention input block and display unit to be merged in a user's interface device 107.This device can be common PC on knee, the handheld devices with dull and stereotyped PC, the such as PDA of touch screen or mobile phone.According to the disposal ability of this device, described processing unit also can be the part of this device.Feedback to patient can comprise one or more following parameter: time in bed, total sleep time, always to awaken the sleep physiology measurement result (hypnogram) of time, Sleep efficiency, sleep latency, the number of times of waking up and persistent period or simplification.

Can by same user interface 108 for medical professional provides feedback.In another embodiment, existence downloads and the data of patient is transferred to the PC (such as, by USB cable, bluetooth, ZigBee or any other communication standard or device) of doctor from patient user interface.In another embodiment, Internet or GSM, UMTS, EDGE, GPRS or any other Internet or mobile phone standard or system can be passed through and the data (automatically) of patient are sent to doctor.

Whole above-mentioned parameter should be contained to the feedback of medical professional.In addition, it also containing patient to the answer of subjective Sleep diaries problem, thus should can contrast subjectivity and objectivity data, and this provides important information by for it, to obtain suitable therapeutic scheme.This point is even more important when sleep state false perception, and sleep state false perception is the insomnia of a type, and at this moment objective dormant data reflects normal sleep mode, but patient self does not but recognize that it falls asleep.

As can be seen from Figure 2, the ECG signal that ECG sensor 200 provides experienced by pretreatment 201, and wherein, pretreatment comprises R blob detection, ectopic beat removal, linear interpolation and with preset frequency (be preferably 4Hz) resampling.Afterwards, consider the PR intervening sequence obtained, and at frequency-domain and time-domain according to criterion evaluation heart rate variability parameter.First, preferably adopt the spectra calculation 202 had on the autoregression model calculating predetermined period of time of advanced trend elimination, the described time cycle is the time series section of 5 minutes centered by the epoch of studied 1 minute.Or, other the method based on Fourier analysis, T/F distribution, time-varying autoregressive can be adopted.In both of the latter cases, in shorter time scale, the ECG R peak that each newly detects such as, upgrade power spectrum estimation.

In 203, use the power spectrum definition spectrum signature in the low-frequency band LF being in preferably 0.04-0.15Hz and the high frequency band HF being preferably 0.15-0.4: make power normalization according to LF_norm=LF/ (LF+HF), and calculate LF/HF ratio.In addition, in 204 be preferably 5 minutes sections predetermined period of time on the time statistical result of RR intervening sequence provide the temporal signatures obtained in 205, such as, pNN 50 (being greater than the percentage ratio of the quantity of the interval difference at the adjacent NN interval of 50ms), SDNN (standard deviation at all NN intervals), SDSD (standard deviation of the successive difference between adjacent spaces), RMSSD (root-mean-square successive difference), RR_mean (average duration at RR interval) and HR_mean (meansigma methods of instantaneous heart rate).

In 206, application two kinds of methods are also passed through from RR sequential extraction procedures interval time nonlinear parameter.In 207 use the first Nonlinear Calculation Method be progressive trend eliminate fluction analysis, its allow on the window that length is 64 gradually integration trend eliminate before signal.In addition, then consider described quadrature signal part and, it will provide obtained differenced time series in 208, new feature 209 will be extracted by it, described feature is defined as the maximum on considered epoch, be preferably the epoch of one minute described epoch, but it can be any predetermined duration epoch.

The second Nonlinear Calculation Method being applied to RR intervening sequence provides multiple dimensioned Sample Entropy.First, described in 210, sequence is the coarseness sequence with yardstick 1 and 2, thus considers 5 minutes sections.In 211, calculating Sample Entropy (hereinafter referred to as sampen) from several ranks of 1 to 10.Thus, in 212, following characteristics is provided: the sampen_scale1_k for rank k=1 to the 10 and sampen_scale2_k for rank k=1 to 10.

As can be seen from the part below Fig. 2, in the bed provided by foil sensor in bed 213, paper tinsel signal experienced by the pretreatment 214 comprising noise reduction and calibration.Low-pass filtering 215 is used to obtain breath signal.Peak identification on this signal allows to derive its respiratory intervals sequence, in 216, also carry out linear interpolation to described sequence, and with preset frequency, preferably with the frequency of 4Hz to its resampling.Preferred use has the autoregression model rated output spectrum 217 that advanced trend is eliminated.Afterwards, split and make its normalization in 218 in low-frequency band (0.04-0.15Hz) and high frequency band (0.15-0.4Hz) to power spectrum, it is used to define spectrum signature LF_norm_respi and LF/HF ratio_respi.In addition, allow exploration (heuristic) index of activity on one minute epoch of definition to the detection of little energy artefact and macro-energy artefact in 219, it is used as again the feature in 220.In addition, the bandpass filtering in 221 provides so-called ballistocardiogram, and it represents mechanical heart activity.This signal can be a kind of interesting substituting, to obtain heart rate variability signals ECG signal.Finally, in 222, RR interval and its respiratory intervals sequence is combined by square coherent function on 5 minute epoch of estimation centered by studied one minute epoch.This coherent function is multiplied with certainly composing of RR intervening sequence, and along frequency axis to its integration.The feature obtained in 223 is the amount of the Coherent Power as percentage ratio.We it is also contemplated that the feature of other assessment cardiopulmonary coupling, such as, and RR interval/its respiratory intervals ratio.

Part on the right of Fig. 2, preferred embodiment contains feature 203,205,209,212,223,218,220, described in features defines the component of the characteristic vector adopted in the categorizing process further illustrated in figure 3.

Fig. 3 is the preferred embodiment of sleep/awakening sorting technique.Step 303 represents the information from characteristic extraction procedure, provides the vector having at least one and belong to the unit of test data set.Judgement is based on the supervised learning grader 301 adopting training dataset 302 to train.Grader 304 is linear or quadratic discriminant grader, support vector machine or k arest neighbors (kNN) grader based on Bayes, and patient wakes or falls asleep to adopt the supervised learning scheme based on training dataset to judge.Training data is more representative, and accuracy and the performance of classification 304 are higher.

Sleep limit treatment is a kind of non-pharmacological methods, can adopt separately its treatment of insomnia patients, also can by itself and pharmacological treatment combined therapy insomnia.In the middle of the people of sleep difference, have one to spend the more time in bed, thus make great efforts the natural tendency providing more sleep chance, this strategy more may cause sleep scattered, of poor quality.

Sleep limit treatment comprises the sleeping time time quantum spent in bed being reduced to reality.Next calculate the adjustment time in bed based on the Sleep efficiency in preset time section, the described time period normally before a week.Such as, if an individual reports spends 8 hours in bed every night, the average length of one's sleep is 6 hours, and the sleeping window of so initial regulation will be 6 hours.

When Sleep efficiency is more than 90%, allowed to postpone in bed time of about 15 to 20 minutes in given one week, if Sleep efficiency is lower than 80%, so reduces identical time quantum, if Sleep efficiency drops between 80% and 90%, so keep stable.Or treatment doctor can set the Sleep efficiency of 85% as the upper limit.Periodically can implement adjustment, such as, adjust weekly, until realize best sleep time.The moving average that the modification implemented in this program can comprise based on Sleep efficiency in (such as) past three to five days changes time in bed, or no matter how Sleep efficiency changes changes all weekly once.This program is by gentle sleep deprivation and reduce sleep anticipatory anxiety and improve sleep continuity.In order to avoid too much day time sleepiness, should be down to deficiency the time on bed five hours every night.

As can be seen from Figure 4, there is a kind of preferred embodiment of the sleep limit treatment method calculated based on Sleep efficiency provided by the invention.

In first step 401, healthcare practitioners initializes wake-up time and bedtime.In step 402, collect the data of five days, it also comprises the information from questionnaire survey.

In step 403, the mean Sleep Efficiency of five days is in the past calculated.In step 404, do artificial situation judgement, judge that mean Sleep Efficiency is lower than between 80%, 80 to 90%, be still greater than 90%.

Suppose that mean Sleep Efficiency is lower than 80%, so in a step 406, require that patient is by time shorten 15 minutes in bed, supposes that mean Sleep Efficiency is greater than 90%, so in step 405, require that patient is by time lengthening 15 minutes in bed.When have collected the sleep info of next five days, again continue this process in step 402.Suppose that mean Sleep Efficiency is between 80 to 90%, give patient's asserts feedback so in step 407, and in a step 408, make wake-up time and bedtime keep one day again, then continue this process in step 403 again.

Also can regard disclosed theory as system 1000, it is constructed to the cognitive behavioral therapy of the patient promoting to suffer from insomnia, and it can utilize following method.Before discussing system 1000 and described method, note following definitions.

Use like this in literary composition, " inconspicuous sensor " refer to sensor be the health not directly being attached to patient's (such as, by binding agent) or described sensor be wireless.The sensor not being coupled to patient body comprises (such as) and is set to and the patient chest closely adjacent or ferrum electret foil that comes in contact, and this is a kind of " inconspicuous sensor ", because it is not attached to patient, patient can move freely.Other " inconspicuous sensor " comprise the bed being coupled to patient piezoresistance strain gauge, embed fabric, such as, ECG sensor in medicated pillow, bedding, nightwear, nightcap or be constructed to the radar/video system detecting motion." the inconspicuous sensor " that can be coupled to patient body comprise any can be installed in wrist strap or like configurations small-sized/light-weight sensors.An example of inconspicuous sensor is like this accelerometer.Any sensor with the line extended between patient and device is not " inconspicuous sensor ".

As adopted in literary composition, " sleep activity data " represent the autonomous or unconscious data measuring action, and be preferably biometric data, described data are patient's state of consciousness, that is, fall asleep or the indication waken.

As used herein, " assessment " refers on certain yardstick, and such as, 1 to 5 answers in the sky is such as very tired of the completely clear-headed one or more questions/statements waited weekly.Such answer is converted to " dividing ", such as, tired=1 point, clear-headed=5 points completely.If relate to a not only problem, so described score combining can be become a score.The action merging described score value can be simple addition, also can comprise multiplier, to seek synergistic combination.Such as, assessment can comprise following three questions/statements: to severity scale (1) difficulty falling asleep of following project, 1=without, 5=is serious, and (2) keep dyskoimesis, 1=without, 5=is serious, and (3) drink how many cups of coffees after 6:00PM.Here, " dividing " relevant with (2) to problem (1) can be merged by simple addition, and the cup number of the coffee such as consumed " dividing " can be then multiplication.In addition, we know, are usually compared by the predetermined threshold that the final merging score of assessment is associated with one or more and described assessment.Such as, an assessment can determine rank or the severe intensity of patient insomnia, and another assessment can determine the type of the insomnia be associated with patient.Assessment through name includes but not limited to: Sleepiness Scale (ESS), sleep before arousal level, sleep disturbance questionnaire, Sleep hygiene custom scale, caffeine quiz, sleep behavior Self-assessment Scale, Glasgow thought content inventory, Glasgow sleep make great efforts scale, Pittsburgh Sleep Quality Index (PSQI) and Multiaxial fatigue inventory (MFI).

As used herein, " threshold value " also can be a numerical range.That is, " threshold value " can exist as the scope of maximum, minima or (no) feasible value.

As shown in Figure 5, the system 1000 being constructed to the cognitive behavioral therapy of the patient promoting to have insomnia comprises communications component 1002, sensing system 1004, first processing unit 1006, second processing unit 1008, the 3rd processing unit 1010, fourth processing unit 1012 and display 1014.Communications component 1002 is constructed to provide electronic communication between the parts pointed out above.That is, each parts is all coupled to communications component 1002, and telecommunication with it.Communications component 1002 and described parts can be wirelessly coupled to various parts.Preferably communications component 1002 is coupled to the electronic communication network 1001 such as, but not limited to Internet and telecommunication with it.

Sensing system 1004 at least has a sensor 1020.Sensing system 1004 is constructed to detect sleep activity data, and provides the sensor system signals combining sleep activity data.As used herein, " sleep activity data " at least comprise heart rate data, breathing rate data and patient body mobile data.Known, can by the data from such sensor 1020, such as, the heart rate data that the exercise data that actigraph detects, ECG detect merges, thus determines or estimate that patient is sleeping or wakes.Again point out, the ECG sensor be associated with disclosed method is inconspicuous sensor, that is, be attached to the sensor in patient garments, instead of traditional with the ECG of electric wire/lead of sensors coupled adhering to patient skin.Other sensors that can adopt include but not limited to be constructed to detect the induction plethysmogram pickup of respiratory movement and are constructed to measure the projection heart and impact the Emfit paper tinsel tracing (cardiomotility, respiratory activity and human motion activity).

Sensing system 1004, each one namely at least one sensor 1020 is coupled to communications component 1002, and electronic communication with it.Each one at least one sensor 1020 is inconspicuous sensor, and it is constructed to produce the sensor signal with the feature that at least one is set forth above.At least one sensor 1020 described can be but be not limited to ECG sensor 1022 and/or activity change record sensor 1024.ECG sensor 1022 is constructed to detect heart rate data and breathing rate data.Heart rate data and breathing rate data are attached to the feature in ECG sensor 1022 signal.Activity change record sensor 1024 is constructed to detect patient body mobile data.Patient body mobile data is attached to the feature in activity change record sensor 1024 signal.Discussed above is the particular type of at least one sensor 1020.Thus, at least one sensor 1020 described can be single accelerometer, single-unit activity change records instrument etc.

Each processing unit, namely, each one in first, second, third and fourth processing unit 1006,1008,1010,1012 comprises arithmetic element, such as it can be but be not limited to Programmable Logic Device (PLC) and communication system (both not shown), and this is well known in the art.The arithmetic element of each processing unit 1006,1008,1010,1012 can also comprise storage device (not shown), such as, it can be but be not limited to random access memory (RAM), read only memory (ROM), flash memory and/or hard disk drive, and above-mentioned memory device can be dish or solid-state components.Described memory device is constructed to store one or more groups executable instruction, hereinafter referred to as routine 1030, also stores the data collected, the data adopting the data that provide of routine 1030 and downloaded by electronic communication network 1001.We know, any data be stored on each processing unit 1006,1008,1010,1012 all can be sent to remote location, such as but not limited to the office of medical professional by communications component 1002 and electronic communication network 1001.Similarly, can by data and/or routine 1030 from remote location, the office such as but not limited to medical professional downloads to each processing unit 1006,1008,1010,1012 by communications component 1002 and electronic communication network 1001.Be appreciated that any function that the equal collaborative work of arithmetic element of each processing unit 1006,1008,1010,1012 is constructed to complete to perform described processing unit.

Preferably the arithmetic element of each processing unit 1006,1008,1010,1012 is arranged in shell (giving indicative icon).The arithmetic element of two or more processing units 1006,1008,1010,1012 can be arranged in shared shell (not shown), these arithmetic elements can collaborative work.That is, such as, the routine 1030 of each processing unit 1006,1008,1010,1012 can be stored on single storage device.But at least one processing unit 1006,1008,1010,1012 has PLC, described PLC non-universal PLC.The PLC of the first processing unit 1006 is not preferably general PLC.

First processing unit 1006 corresponds to above-described pretreatment unit 102.That is, the first processing unit 1006 is constructed to perform the step be associated with pretreatment unit 102 mentioned above.First processing unit 1006 is coupled to communications component 1002 and electronic communication with it.First processing unit 1006 is constructed to by communications component 1002 receiving sensor system signal, and sleep activity data are converted to sleep pattern data.The dozing time that sleep pattern data comprises patient's time in bed, patient total length of one's sleep, patient total awakening time, the Sleep efficiency of patient, the hypnagogic latency time of patient, patient start the awakening after falling asleep and patient.As used herein, " time in bed " refers to that patient spends the time in bed, no matter falls asleep and still wakes.As used herein, " total sleep time " is the part that in time in bed, patient falls asleep.As used herein, " hypnagogic latency time " is that patient goes to bed the time between falling asleep for the first time.As used herein, " awakening after sleep " be in time in bed patient sleeping from its first time after wake up until the part in the moment of waking up its last morning.As used herein, " dozing time " refers to the persistent period from finally waking up to the time that patient gets up.As used herein, the part that the patient in the time referring in bed that " always awakens the time " wakes.As used herein, " Sleep efficiency " refers to the ratio of total sleep time and time in bed.As used herein, " awakening after sleep " refer to the number of times that patient wakes up after first time is sleeping.Thus, first processing unit 1006 be constructed to process heart rate data, breathing rate data and patient body mobile data, with determine following at least one: the awakening after the Sleep efficiency of the total sleep time of patient's time in bed, patient, total awakening time of patient, patient, the hypnagogic latency time of patient, patient falls asleep and the dozing time of patient.

Second processing unit 1008 corresponds to user's interface device 107 mentioned above.That is, the second processing unit 1008 is constructed to perform the step relevant to user's interface device 107 mentioned above.Second processing unit 1008 preferably includes display 1014.Second processing unit 1008 comprises the routine 1030 being constructed to show user interface 108 on display 1014.Second processing unit 1008 preferably includes input module 1034, it has one or more input equipment, such as its can be but be not limited to keyboard 1036 and mouse 1038 (or tracking ball, touch screen or any other device of the function similar with mouse is provided).Known, patient can input patient by input module 1034 and user interface 108 and input data.User interface 108 is constructed to present the one or more assessment hereafter discussed, and allows user to input data in Sleep diaries.Second processing unit 1008 is coupled to communications component 1002 and electronic communication with it.

3rd processing unit 1010 corresponds to feature extraction unit 103 mentioned above, sleep/awakening grader unit 104 and Sleep efficiency computing unit 106.That is, the 3rd processing unit 1010 is constructed to perform the step be associated with above-mentioned feature extraction unit 103, sleep/awaken grader unit 104 and Sleep efficiency computing unit 106.3rd processing unit 1010 is coupled to communications component 1002 and electronic communication with it.3rd processing unit 1010 is constructed to receive sleep pattern data by communications component 1002 and patient inputs data, and to its execution analysis.Described analysis creates patient sleeps's profile analysis (profile).As used herein, " patient sleeps's profile analysis " is the subjective data that the objective data of the health mobile data comprising such as patient and such as patient input the data of data.Patient sleeps's profile analysis comprises the sleep history of patient, sleep pattern, the quantitative and qualitative analysis tolerance of insomnia, the identification of insomnia's correlative factor and insomnia's consequence identification in the daytime.In the daytime the consequence of insomnia include but not limited in the daytime poor, the cognitive function of energy (such as, wholwe-hearted, pay close attention to, attention and memory) difference and emotion and mobility poor.

Include but not limited to the subjective estimation to sleep quality to the qualitative measure of insomnia, such as, difference to excellent, or takes the Likert Scale of 1 to 10.To the quantitative measurement of insomnia include but not limited to hereafter by insomnia's severity index (ISI) score discussed in detail, the subjectivity and objectivity of hypnagogic latency time is assessed, fall asleep after awakening and dozing time.Such insomnia's quantitative measurement and level threshold value can be compared, such as, hypnagogic latency time threshold value is set to 30 minutes.Can use and so relatively assess sleep disturbances type (sleep problem, sleep Preserving problems or early awakening problem etc.).In addition, the subjectivity and objectivity of Sleep efficiency can be estimated compare with level threshold value (such as 90%), whether sleep in the mode consolidated with assess patient.

3rd processing unit 1010 to be constructed to sleep pattern data by electronic communication network 1001, patient inputs data and patient sleeps's profile analysis sends to medical professional.

Fourth processing unit 1012 corresponds to above-mentioned sleep limit algorithm device 106.That is, fourth processing unit 1012 is constructed to perform the step be associated with above-mentioned sleep limit algorithm device 106.Fourth processing unit 1012 is coupled to communications component 1002 and electronic communication with it.Fourth processing unit is constructed to analyze patient sleeps's overview.Hereafter the analysis that performs of fourth processing unit 1012 will be discussed.Fourth processing unit 1012 is also constructed to provide the treatment course for the treatment of relevant to patient sleeps's overview.That is, fourth processing unit 1012 comprises storage device 1050, and it has the multiple interactive therapy be stored thereon and instructs module 1060,1062,1064,1066,1068,1070.

Treatment instructs module 1060,1062,1064,1066,1068,1070 usually relevant to insomnia's correlative factor.Insomnia's correlative factor includes but not limited to behavior and perceptional factors.Such as, behavial factor includes but not limited to sleep restriction, bedtime arrangement and awakening time.Behavial factor can indicate the change of patient mode, erratic sleep/awakening time and/or patient and when enter a kind of compensation policy, such as, early goes to bed and/or rises evening and/or nap, thus making compensation to not sleeping sound night.Behavial factor also comprise stimulate control, that is, bed-sleep relatedness or when bed and sleep between conditional relationship at the end of.That is, bed and bedtime become movable hint instead of sleep hint.Such as, patient may lie on a bed and wake, and this has obscured bed and sleep contact in the brain, thus people can not be fallen asleep.Before other behavial factors comprise sleeping of patient daily practice and other may affect the custom of sleep, such as, wine, coffee, nicotine, dinner custom, bedroom environment etc.Perceptional factors includes but not limited to the brains hyperkinesia of patient at night, namely, bedtime hyperarousal and/or worry, anxiety about sleep, such as, to the amplification of insomnia's consequence, sleep perceive control and predictive ability, unpractical expection to sleep.

Each interactive therapy instructs module 1060,1062,1064,1066,1068,1070 containing three parts: (1) treatment suggestion, provide at the first day of patient's access modules, (2) at the general information that remaining each sky of described module is sent with the form that every day points out, and (3) can access the target making part of the first day use of described module patient.Suggestion/intervention that treatment suggestion comprises based on standard C BT-I.Treatment suggestion and prompting every day accurately find problem to be continued or those more serious factors with regard to particular patient, and can be relevant to the target that patient assert.That is, although target that the is overall or overall situation is improving water flood, patient can understand concrete problem, and assert the objectives relevant to this problem.Such as, patient falls asleep after may wishing to learn to fall asleep more easily and/or learn to wake up in the middle of sleep cycle more easily again.Or, patient may wish how study feels more spiritual in the morning, improve their energy in the daytime, improve their behavior expression in the daytime, improve their cognitive function (wholwe-hearted, concentrate, attention and memory) and/or improve their emotion and mobility.Or substitute as another kind, patient may wish to be familiar with their actual sleep pattern or determine that they slept how many hours at night.Described guidance and/or other suggestions can be relevant to global object or one or more more objectives.

Fourth processing unit 1012 is also constructed to the selection and the order that instruct module based on the sleep overview tissue treatment of patient.Finally, fourth processing unit 1012 is constructed to the treatment through tissue to instruct module to be presented on display 1014.

Display 1014 is coupled to communications component 1002 and electronic communication with it.Display 1014 is constructed to present user interface 108 discussed above and any other information.

Before the method be associated with system 1000 is discussed, note following assessment.Described assessment or its various piece will be used in the various analytic processes hereafter discussed.A known assessment is insomnia's severity index (ISI), and it comprises following problems/statement:

Insomnia's severity index

Another known assessment, namely insomnia's frequency questionnaire (IFQ) comprises and shows that patient suffers weekly the answer of the natural law of the puzzlement of pointed symptom, also comprises following problems/statement:

Sleep diaries not only comprised carry out the questions/statements of marking according to certain scale but also comprise to its answer be according to hour and the questions/statements of time period of minute metering.Sleep diaries comprises following problems/statement:

Sleep diaries

Acquiescence Sleep diaries (baseline estimate and program in the middle of):

1, please mark to the sleep quality of your last night:

Adopt scale 1-5, non-constant ... fabulous

Do 2, waking up this morning, how you feel?

Adopt scale 1-5, exhausted ... full of energy

Does 3, last night, when you turned off lamp start sleep?

→ according to hh:mm input time

Have 4, you used and how long have fallen asleep? → input by minute in units of time

Does is 5, when you finally wake up this morning? → according to hh:mm input time

6, please estimate total time amount that you wake at night (by minute in units of) (from first time, you fall asleep the moment of finally waking up to this morning) → by minute in units of input time

Did 7, this morning, when you get up? → according to hh:mm input time

Does is 8, what kind of the degree of fatigue of your yesterday?

Adopt scale 1-5, very tired ... very clear-headed

9, excuse me your sleep of abnormal extraneous factor was there yesterday? (such as, health, work, household, environment etc.)

Adopt scale 1-5: a lot of monkey wrench ... do not have monkey wrench

Another sleep evaluation is DBAS-16, and it relates to the confidence of patient about sleep, and comprises patient to including but not limited to the statement that following content is marked:

List the idea of several reflection people about sleep and the statement of attitude below.Please point out that you individual agrees to or dissenting degree each statement.There is no the answer of right or wrong.For every statement, the numeral of the individual idea corresponding to you please be iris out.Please answer all projects, even if some projects may not be directly applied for you self situation.

Come down hard upon strong approval

1. I needs to sleep for 8 hours and could to feel spirit in the daytime and well and normally work.

2. when I must be taken a nap when given night does not sleep in right amount for second day or second night is slept longer time bias.

3. I worries that long-term insomnia causes serious consequence to my healthy.

4. I worries to lose the control to sleep ability.

5., after having a bad night, I knows that this will cause interference at second day to my daily routines.

6., in order to clear-headed in the daytime and work well, I thinks and eats sleeping pill still than sleeping a night bad more better.

7. I feel by day to be easily stung to fury, depressed or anxiety, it is not mainly because I sleeps last evening.

8., when I has not slept an evening, I knows that this will cause interference to arranging the length of one's sleep of my whole week.

9. do not have enough sleep evening, second day my almost cisco unity malfunction.

10. I always can not predict evening and have a good sleep or bad.

The negative consequences that 11. my sleeps that almost do not have Capacity Management to be interfered cause.

12. when I feel tired daytime, there is no energy or just seem not work well time, its be usually all because I do not sleep last evening.

13. I think that insomnia is in fact the result of chemical unbalance.

14. I feel the ability that insomnia is ruining me and enjoys life, and the thing hindering me to do to feel like doing.

15. medicines may be unique solutions of insomnia.

16. I an evening sleep bad after avoid or cancel performance of a duty (society, family).

Utilize the system 1000 being constructed to the cognitive behavioral therapy of the patient promoting to have insomnia, patient can receive the insomnia's cognitive behavioral therapy according to the method with following step.Start, described method comprises the step utilizing sensing system 1004 to monitor 2000 patient sleeps, and described sensing system 1004 has at least one inconspicuous sensor 1020.As mentioned above, sensing system 1004 is constructed to detect sleep activity data.Described method also comprise collect 2002 patients input data, merge 2006 patients input data and sleep activity data creating patient sleeps's overview, analyze 2008 patient sleeps's overviews with determine the treatment course for the treatment of and by described treatment course for the treatment of for patient presents 2010 to the step on display 1014.

The step utilizing the sensing system with at least one inconspicuous sensor to monitor 2000 patient sleeps comprises the step utilizing 2020ECG sensor and activity change record sensor.As mentioned above, ECG sensor 1022 is constructed to detect heart rate data and breathing rate data, and activity change record sensor 1024 is constructed to detect patient body mobile data.The step utilizing the sensing system with at least one inconspicuous sensor to monitor 2000 patient sleeps also comprise process 2022 heart rate data, breathing rate data and patient body mobile data with determine following in the step of at least one: the awakening after the Sleep efficiency of the total sleep time of patient's time in bed, patient, total awakening time of patient, patient, the hypnagogic latency time of patient, patient falls asleep and the dozing time of patient.

Collect step that 2002 patients input data comprise the steps: to collect 2030 about patient sleeps's history data, collect 2032 about patient's medical history data, make patient complete 2034 insomnia's severity index assessments and make patient 2036 preserve Sleep diaries.As mentioned above, by the second processing unit 2008, more specifically, input patient by input module 1034 and user interface 108 and input data.

Analyze 2008 patient sleeps's overviews and can comprise the steps: that analysis 2038 patient inputs data and sleep activity data to identify the factor causing patient insomnia to determine to treat the step of the course for the treatment of; Identify the type of 2039 insomnias be associated with patient; Analyze 2040 patients and input data and sleep activity data to determine the order of severity of patient insomnia; Analyze 2042 patients and input data to determine the order of severity of patient insomnia on the impact of day's activities; Analyze 2044 patients and input data to determine the type that patient is insomniac; And based on the order of severity of the insomnia of patient, the insomnia of patient the order of severity and the insomniac type of patient to advise in below 2046 to patient one is affected on day's activities: introduce medical professional and treat, advise interactive therapy course for the treatment of, non-recommended therapy to patient.

If recommend interactive therapy course for the treatment of to patient, so preferably by the display 1014 presenting user interface 108, described treatment is supplied to patient.That is, analyze 2008 patient sleeps's overviews to have to determine to treat the course for the treatment of and described treatment to be presented the step that 2010 steps on display 1014 comprise the course for the treatment of: provide more than 2050 interactive therapy to instruct module based on patient sleeps's overview, each module is relevant to the one side of insomnia; Determine that 2052 which interactive therapy instruct module relevant to the insomnia of patient; Determine that 2054 interactive therapy of presenting to patient instruct the order of module; Described interactive therapy instruct module to present to 2056 patients according to predetermined order by display 1014.

As mentioned above, each interactive therapy instructs module 1060,1062,1064,1066,1068,1070 containing three parts: (1) treatment suggestion, provide at the first day of patient's access modules, (2) at the general information that remaining each sky of described module is sent with the form that every day points out, and (3) can access the target making part of the first day use of described module patient.Described multiple interactive therapy instructs module to comprise and arranges module 1060, bed-sleep relatedness module 1062, Cognitive reconstruction module 1064, coping strategy module 1066 length of one's sleep, loosens module 1068 and life style module 1070.Except the general information instructing module 1060,1062,1064,1066,1068,1070 to be associated with each interactive therapy discussed above, each one in described concrete module also has the extra process that will hereafter discussing.Also to point out, described interactive therapy instructs module 1060,1062,1064,1066,1068,1070 can be stored in fourth processing unit 1012 by (1), namely, be stored in the storage device of fourth processing unit 1012, (2) downloaded in fourth processing unit 1012 by electronic communication network 1001 and communications component 1002, or (3) its combination.That is, fourth processing unit 1012 can store the part that each interactive therapy instructs module 1060,1062,1064,1066,1068,1070, and can have the extraneous information upgrading or also have download.

Such as, if recommend to patient and arrange module 1060 length of one's sleep, the further step so providing the method for the cognitive behavioral therapy of insomnia to comprise has: the length of one's sleep providing 2060 to be constructed to determine to arrange score first and second lengths of one's sleep arranges assessment; Determine that first and second lengths of one's sleep of 2062 patients arrange score; And determine whether 2064 patients have the arrangement erratic length of one's sleep.The determination 2062 of score that arranges for first and second lengths of one's sleep of patient can based on both objective data and subjective data.Such as, as discussed above, objective data is such as but is not limited to the Sleep efficiency data of sensor collection.Subjective data can be evaluated as basis, and such as, described assessment can be following problems/statement:

Between-my the regular WA for oneself setting every morning, no matter how (from strongly not agreeing to strongly approve of scoring) my sleep quality and persistent period;

-when I am not when being slept in right amount an evening of specifying, I must be compensated (from strongly not agreeing to strongly approve of scoring) by nap in second day or second night longer time of sleeping; And

-scoring is converted to score further.

Give score by (such as) for answer and described score and objective data are combined these are marked and arrange score first and second lengths of one's sleep being converted to patient.Based on determined factor, such as, if arrange first length of one's sleep of patient score to be greater than arrange threshold value first length of one's sleep, so described method also comprises provided arrange guidance 2066 lengths of one's sleep, and/or, and if arrange second length of one's sleep of patient the erratic length of one's sleep and arrange score to be greater than to arrange threshold value second length of one's sleep, so provide and arrange for 2068 lengths of one's sleep to teach if patient has.In this module 1060 and other all modules 1062,1064,1066,1068,1070, complete guidance step by providing information to patient.Information is provided with forms such as text message, e-mail, instant messages preferably by display 1014.As discussed above, teach message preferably to have prompting, recommend form such as treatment action, progress report etc., and usually relevant to target.

If recommend bed-sleep relatedness module to patient, the additional step so providing the method for the cognitive behavioral therapy of insomnia to comprise has: the bed-sleep relatedness of bed-sleep relatedness score is assessed to provide 2070 to be constructed to determine; Determine the bed-sleep relatedness score of 2072 patients; And if the bed of patient-sleep relatedness score is greater than bed-sleep relatedness threshold value, 2074-sleep relatedness is so provided to teach.Bed-sleep relatedness assessment can comprise such questions/statements, such as:

Please to point out that before sleeping you in bed or the thing how long done in bedroom once.Please consider how doing of an average week, then complete form:

-see TV, work, with PC, listen to the radio or music, talks with someone, make a phone call, eat and get something to drink, read a book or magazine: never/seldom/sometimes/frequent/quite usual;

-I am once light-off of going to bed: never/and seldom/sometimes/often/quite usual; And

-I spend a lot of time to lie on a bed and wake evening: never/seldom/sometimes/often/be quite usually converted into the bed-sleep relatedness score of patient.

If Cognitive reconstruction module 1064 is recommended patient, the further step so providing the method for cognitive behavioral therapy to comprise for insomnia has: provide 2080 be constructed to determine the first sleep Concept Evaluation of the first sleep concept score and determine the first sleep concept score of 2082 patients.If the first sleep concept score of patient is greater than the first sleep concept threshold value, so perform 2,084 second sleep Concept Evaluations, to determine to amplify score, perceive control score and unpractical expection score.There is provided the targeting relevant to any aspect of Concept Evaluation of sleeping to teach, wherein, patient is on dependent thresholds.That is, if the amplification score of patient is greater than amplification threshold value, so provide 2086 amplifications to teach.If the perceiveing of patient controls score and be greater than and perceive control threshold value, so provide 2088 to perceive and control to teach.In addition, if unpractical expection score of patient is greater than unpractical expection threshold value, 2089 unpractical expections are so provided to teach.Sleep Concept Evaluation can comprise such questions/statements, such as:

Qa: " I makes too many effort to fall asleep at night, and this should be natural thing; "

Qb: " cannot fall asleep if I lies on a bed evening, will worry oneself to can't fall asleep; " and

Qc: " I just starts before going to bed at night to worry sleep "

And from the problem of ISI, IFQ and DBAS-16, these problems will be converted into the first and second sleep concept scores of patient.

If recommend coping strategy module to patient, the further step so providing the method for the cognitive behavioral therapy of insomnia to comprise has: the first coping strategy of coping strategy score is assessed to provide 2090 to be constructed to determine; Determine 2092 coping strategy scores; And if the coping strategy score of patient is greater than coping strategy threshold value, 2094 coping strategys are so provided to teach.Coping strategy assessment can comprise such questions/statements, such as:

The negative consequences that-sleep that I does not almost have ability to overcome to be perplexed causes: anything but like this, seldom like this, sometimes like this, often like this, almost always like this;

-I feel the ability that sleeping problems are destroying me and enjoy life, and the thing hindering me to feel like doing (society/occupation/home duties); And

-I avoids or cancel oneself responsibility (society, family) after the sleep very poor through a night, and it will be converted into the coping strategy score of patient.

If will loosen module to recommend patient, the further step so providing the method for the cognitive behavioral therapy of insomnia to comprise will have: what provide 2100 to be constructed to determine score transit time, worry time score and behavior score loosens assessment; Determine 2102 transit time score, worry time score and behavior score.If score transit time of patient is greater than threshold value transit time, so described method comprises the step providing teach for 2104 transit times.If the worry time score of patient is greater than worry time threshold, so described method comprises the step providing for 2106 worry times taught.If the behavior score of patient is greater than behavior asset pricing, so described method comprises the step providing 2108 behaviors to teach.Loosen assessment and can comprise such questions/statements, such as:

What Q1: you do usually before going to bed? see TV, reading, work, use PC, do housework, other (check boxes);

Q2: you think you go to bed before activity be daily convention? Yes/No;

Q3: when you attempt how long experience a following problems when falling asleep:

My brain thinks thing with tossing about always;

My state of mind always will just can be loosened (unwind) for a long time; And

I cannot empty my thought

(never, seldom, sometimes, often, very common)

Q4: how long you feel once to be difficult to " let alone " on health and loosen your health? never, seldom, sometimes, often, very common

Q5: the program loosened before you go to bed? and

Q6: you think that high pressure/high workload amount result in sleep and is deteriorated?

It loosens score by what be converted into patient.

If recommend life style module to patient, the further step so providing the method for the cognitive behavioral therapy of insomnia to comprise has: provide 2110 to be constructed to determine body movement score, ethanol score, coffee score, nap score, nicotine score, take in the lifestyle assessment of score (i. e. and/or energy intake score) and environment score; And determine 2111 body movement scores, ethanol score, coffee score, nap score, nicotine score, absorption score and environment score.If the body movement score of patient is greater than body movement threshold value, so described method comprises the step providing 2112 body movemenies to teach.If the ethanol score of patient is greater than ethanol threshold value, so described method comprises the step providing 2114 ethanol to teach.If the caffeine score of patient is greater than caffeine threshold value, so described method comprises the step providing 2116 caffeine to teach.If the nap score of patient is greater than nap threshold value, so described method comprises the step providing 2118 naps to teach.If the nicotine score of patient is greater than nicotine threshold value, so described method comprises the step providing 2120 nicotine to teach.If the absorption score of patient is greater than or less than take in threshold value (or scope), so described method comprises the step providing 2122 absorptions to teach.That is, if patient's overfeed or very little, so patient will exceed absorption threshold range.If the environment score of patient is greater than environmental threshold value, so described method comprises the step providing 2124 environment to teach.Lifestyle assessment can include but not limited to such questions/statements, such as:

How long Q1: how long you take exercise once, take exercise? X time weekly, y minute;

Q2: you take exercise in 4 hours before going to bed?

Q3: you drink how many glasss of wine evening?

The wine (check box) of X cup medicated beer-wine-stronger;

Q4: you drink every day, and are how many cups the beverage (coffee, tea, cola) containing caffeine? X cup, wherein y cup is at night;

Q5: you take a nap? x time weekly, y minute;

Q6: when you take a nap usually? 4pm in the past/after (check box);

Q7: your do you do weed? every day x cigarette;

Q8: you are do you do weed before going to bed? night? Yes/No;

Q9: you wake up at night? you are frequent:

Eat?

Drink something? And

Go to toilet?

Q10: your meal time rule?

Q11: whether you feel too hot or too cold on bed sometimes?

Q12: you like your bedroom?

Q13: your sleep is subject to bothering of noise sometimes? And

Q14: your sleep is subject to the interference of light sometimes?

Be converted into the body movement score of patient, ethanol score, caffeine score, nap score, nicotine score, take in score and environment score.

As mentioned above, and as shown in Figure 6, described method is preferably iterative process, wherein, upgrades the sleep overview of 2130 patients, thus current insomnia's level of reflection patient.Although can every day more new patient input data and sleep activity data, preferably upgrade interactive therapy course for the treatment of regularly, and frequent unlike upgrading every day.Preferably upgrade weekly interactive therapy course for the treatment of.Once have updated patient sleeps's overview of patient, with regard to replicate analysis 2008 patient sleeps overview to determine the step for the treatment of the course for the treatment of.That is, following step and any sub-step can be repeated: analyze 2040 patients and input data and sleep activity data to determine the order of severity of the insomnia of patient; Analyze 2042 patients and input data to determine the order of severity of the insomnia of patient on the impact of day's activities; Analyze 2044 patients and input data to determine the type that patient is insomniac; And based on the order of severity of the insomnia of patient, the insomnia of patient on the order of severity of the impact of day's activities and the insomniac type of patient, recommend one of 2046 following contents to patient: introduce medical professional and treat; Interactive therapy course for the treatment of is recommended to patient; And not recommended therapy.

In addition, due to the 3rd processing unit 1010 be constructed to send sleep pattern data by electronic communication network 1001 to medical professional, patient inputs data and patient sleeps's overview, thus analyze 2008 patient sleeps's overviews and can also comprise the steps: to make that medical professional checks 2050 sleep pattern data, patient inputs data and patient sleeps's overview to determine to treat the step of the course for the treatment of, and make medical professional provide 2052 directly to feed back by electronic communication network 1001 to patient.These steps can be repeated after inputting data and patient sleeps's overview renewal sleep pattern data, patient.

One of skill in the art will recognize that much fall change within the scope of the invention and amendment.This method and system is preferably mainly used in the patient in the insomniac of family or family or hotel, and under hospital environment, in transport process or the flowing patient of family, but also may be applied to inpatient.And, also can utilize the present invention with the device that Healthy People and even animal are object.In addition, although by accompanying drawing and above-mentioned explanation to invention has been detailed diagram and description, should such diagram and description be regarded as illustrative and exemplary, instead of restrictive.The invention is not restricted to the disclosed embodiments.

By research accompanying drawing, description and claims, those skilled in the art can to understand in the middle of the process of the present invention for required protection and to implement other modification for the disclosed embodiments putting into practice.In the claims, " comprising " one word do not get rid of other elements or step, indefinite article " " does not get rid of plural number.State that some measure does not represent the combination that advantageously can not adopt these measures in mutually different dependent claims.Any Reference numeral in claim should not be regarded as the effect with limited field.

Although describe specific embodiments of the invention in detail, those skilled in the art will recognize that and can develop various amendment to these details and replacement scheme according to overall teachings of the present disclosure.Correspondingly, with regard to scope of the present invention, disclosed concrete layout is exemplary and nonrestrictive, and described scope has been endowed whole width of claim and equivalency thereof.

Claims (5)

1. one kind is constructed to the system (1000) of the cognitive behavioral therapy of the patient promoting to have insomnia, and described system comprises:
Communications component (1002), it is constructed to provide electronic communication;
There is the sensing system (1004) of at least one sensor (1020), described sensing system (1004) is constructed to detect sleep activity data, and the sensor system signals combining described sleep activity data is provided, described sensing system (1004) is coupled to described communications component (1002) and electronic communication with it;
First processing unit (1006), it is coupled to described communications component (1002) and electronic communication with it, described first processing unit (1006) is constructed to receive described sensor system signals, and described sleep activity data are converted to sleep pattern data;
Second processing unit (1008), it has input module (1034) and is constructed to collect patient and inputs data, and described second processing unit (1008) is coupled to described communications component (1002) and electronic communication with it;
3rd processing unit (1010), it is coupled to described communications component (1002) and electronic communication with it, described 3rd processing unit (1010) is constructed to receive described sleep pattern data and described patient inputs data, and creates patient sleeps's overview thus to its execution analysis;
Fourth processing unit (1012), it is coupled to described communications component (1002) and electronic communication with it, described fourth processing unit (1012) is constructed to analyze described patient sleeps's overview, and provides the treatment course for the treatment of relevant to described patient sleeps's overview; And
Display (1014), it is coupled to described communications component (1002) and electronic communication with it, and described display (1014) is constructed to present user interface (107).
2. system according to claim 1, wherein, described sensing system (1004) comprises at least one sensor (1020), described in each, at least one sensor (1020) is inconspicuous sensor, it is configured to produce the sensor signal with at least one feature, and wherein said inconspicuous sensor is not directly attached to the health of patient or wireless sensor.
3. system according to claim 2 (1000), wherein:
Described at least one sensor (1020) comprises ECG sensor (1022) and activity change record sensor (1024);
Described ECG sensor (1022) is constructed to detect heart rate data and breathing rate data, and described heart rate data and breathing rate data are attached to the feature in ECG sensor signal;
Described activity change record sensor (1024) is configured to detect patient body mobile data, and described patient body mobile data is attached to the feature in activity change record sensor signal;
Described first processing unit (1006) is configured to extract described heart rate data and breathing rate data from described ECG sensor signal;
Described first processing unit (1006) is configured to extract described patient body mobile data from described activity change record sensor signal; And
Described first processing unit (1006) is configured to process described heart rate data, breathing rate data and patient body mobile data, with determine following at least one: the awakening after the total sleep time of described patient time in bed, described patient, total awakening time of described patient, the Sleep efficiency of described patient, the hypnagogic latency time of described patient, described patient falls asleep and the dozing time of described patient.
4. system according to claim 1 (1000), wherein
Described 3rd processing unit (1010) is coupled to electronic communication network (1001) and electronic communication with it; And
Described 3rd processing unit (1010) is constructed to send described sleep pattern data by described electronic communication network (1001) to medical professional, described patient inputs data and described patient sleeps's overview.
5. system according to claim 1 (1000), wherein:
Described fourth processing unit (1012) comprises and has the storage device (1050) that module is instructed in the multiple treatments be stored thereon, module (1060 is instructed in described treatment, 1062,1064,1066) be configured to be presented on described display (1014);
Described fourth processing unit (1012) is also configured to organize described treatment to instruct module (1060,1062,1064,1066) based on described patient sleeps's overview; And
Described fourth processing unit (1012) is also configured to the described treatment through tissue to instruct module to be presented on described display (1014).
CN201080024189.8A 2009-06-04 2010-06-01 Method and system for providing behavioural therapy for insomnia CN102448368B (en)

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